In December 2015, a published paper rocked the deep learning world. This paper is widely regarded as one of the most influential papers in modern deep learning and has been cited over 110,000 times. The name of this paper was Deep Residual Learning for Image Recognition (aka, the ResNet paper). In this session, we’ll take a brief tour through the history of computer vision, into the anatomy of a convolutional neural network, understand their limitations, and learn how the ResNet paper changed deep learning forever.
By the end of the session, you’ll know:
• What computer vision was like before convolutional neural networks (CNNs)
• The anatomy of CNNs
• The limitations of CNNs
• Residual networks and the skip connection
• How to perform image classification with ResNet with code
We are looking for passionate people willing to cultivate and inspire the next generation of leaders in tech, business, and data science. If you are one of them get in touch with us!